BACKGROUND: Trauma registries are an essential but expensive tool for monitoring trauma system performance. The time required to catalog patients' injuries is the source of much of this expense. Typically, 15 minutes of chart review per patient are required, which in a busy trauma center may represent 25% of a full-time employee. We hypothesized that International Classification of Disease-Ninth Revision (ICD-9) codes generated by the hospital information system (HI) would be similar to those coded by a dedicated trauma registrar (TR) and would be as accurate as TR ICD-9 codes in predicting outcome. METHODS: One thousand eight hundred twelve patients admitted to a Level I trauma center during 2 years had International Classification of Disease Injury Severity Scores (ICISS) calculated based on HI and TR ICD-9 codes. The relative predictive powers of these two ICISSs were then compared for every patient using Receiver Operator Characteristic Curve Area (ROC) and Hosmer Lemeshow Statistics. RESULTS: Eighty-nine percent of patients (1,608 of 1,812) had identical HI and TR ICISSs. Eleven patients' ICISSs differed by >0.1, and only two patients' scores differed by >0.2. ICISS proved to be a powerful predictor of outcome whether derived from HI (ROC = 0.884; 95% confidence interval (CI) = 0.850-0.917) or TR (ROC = 0.872; 95% CI = 0.837-0.908). Although these predictive powers were not significantly different (p = 0.076), the trend was for HI to perform better than TR. ISS calculated for the same data set using the MacKenzie dictionary proved significantly less predictive of outcome than either ICISS (ROC(MacKenzie) = 0.843; 95% CI = 0.792-0.884; p = 0.034). CONCLUSION: We conclude that in our hospital TR data on individual injuries can be replaced by HI data without loss of predictive power. ISS based on the MacKenzie dictionary should be abandoned because it is much less predictive of outcome than ICISS.
BACKGROUND:Trauma registries are an essential but expensive tool for monitoring trauma system performance. The time required to catalog patients' injuries is the source of much of this expense. Typically, 15 minutes of chart review per patient are required, which in a busy trauma center may represent 25% of a full-time employee. We hypothesized that International Classification of Disease-Ninth Revision (ICD-9) codes generated by the hospital information system (HI) would be similar to those coded by a dedicated trauma registrar (TR) and would be as accurate as TR ICD-9 codes in predicting outcome. METHODS: One thousand eight hundred twelve patients admitted to a Level I trauma center during 2 years had International Classification of Disease Injury Severity Scores (ICISS) calculated based on HI and TR ICD-9 codes. The relative predictive powers of these two ICISSs were then compared for every patient using Receiver Operator Characteristic Curve Area (ROC) and Hosmer Lemeshow Statistics. RESULTS: Eighty-nine percent of patients (1,608 of 1,812) had identical HI and TR ICISSs. Eleven patients' ICISSs differed by >0.1, and only two patients' scores differed by >0.2. ICISS proved to be a powerful predictor of outcome whether derived from HI (ROC = 0.884; 95% confidence interval (CI) = 0.850-0.917) or TR (ROC = 0.872; 95% CI = 0.837-0.908). Although these predictive powers were not significantly different (p = 0.076), the trend was for HI to perform better than TR. ISS calculated for the same data set using the MacKenzie dictionary proved significantly less predictive of outcome than either ICISS (ROC(MacKenzie) = 0.843; 95% CI = 0.792-0.884; p = 0.034). CONCLUSION: We conclude that in our hospital TR data on individual injuries can be replaced by HI data without loss of predictive power. ISS based on the MacKenzie dictionary should be abandoned because it is much less predictive of outcome than ICISS.
Authors: Daniel W Spaite; Chengcheng Hu; Bentley J Bobrow; Vatsal Chikani; Duane Sherrill; Bruce Barnhart; Joshua B Gaither; Kurt R Denninghoff; Chad Viscusi; Terry Mullins; P David Adelson Journal: JAMA Surg Date: 2017-04-01 Impact factor: 14.766
Authors: Hideo Tohira; Ian Jacobs; David Mountain; Nick Gibson; Allen Yeo Journal: Scand J Trauma Resusc Emerg Med Date: 2012-09-10 Impact factor: 2.953
Authors: Kjetil G Ringdal; Timothy J Coats; Rolf Lefering; Stefano Di Bartolomeo; Petter Andreas Steen; Olav Røise; Lauri Handolin; Hans Morten Lossius Journal: Scand J Trauma Resusc Emerg Med Date: 2008-08-28 Impact factor: 2.953